https://github.com/yevheniidatsenko/goit-cs-hw-05

💾 Hometask - Computer Systems (Asynchronous Processing)
https://github.com/yevheniidatsenko/goit-cs-hw-05

async-await asynchronous-programming asyncio goit-cs-hw-05 python3

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💾 Hometask - Computer Systems (Asynchronous Processing)

• Host: GitHub
• URL: https://github.com/yevheniidatsenko/goit-cs-hw-05
• Owner: yevheniidatsenko
• Created: 2024-12-14T09:27:50.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-14T21:31:29.000Z (about 1 month ago)
• Last Synced: 2024-12-14T22:25:15.170Z (about 1 month ago)
• Topics: async-await, asynchronous-programming, asyncio, goit-cs-hw-05, python3
• Language: Python
• Homepage:
• Size: 3.34 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Homework for the Module "Asynchronous Processing"

## Technical Task Description

The homework consists of two independent tasks.

---

### Task 1

Write a Python script that reads all files in a user-specified source folder and organizes them into subfolders in a target folder based on their file extensions. The script must perform sorting asynchronously to efficiently handle a large number of files.

#### Step-by-Step Instructions

1. Import the necessary asynchronous libraries.

2. Create an `ArgumentParser` object to handle command-line arguments.

3. Add the required arguments for defining the source and target folders.

4. Initialize asynchronous paths for the source and target directories.

5. Write an asynchronous function `read_folder` that recursively reads all files in the source folder and its subfolders.

6. Write an asynchronous function `copy_file` that copies each file to the corresponding subfolder in the target folder based on its file extension.

7. Set up error logging.

8. Run the asynchronous `read_folder` function in the main block.

**Acceptance Criteria**

- The code performs asynchronous reading and copying of files.

- Files are organized into subfolders based on their extensions.

- The program processes command-line arguments.

- All errors are logged.

- The code is readable and adheres to PEP 8 standards.

#### Result - Task 1

![Solution](./scr_task_01.png)

---

### Task 2

Write a Python script that downloads text from a given URL, analyzes word frequency in the text using the MapReduce paradigm, and visualizes the top words with the highest frequency.

#### Step-by-Step Instructions

1. Import the necessary modules (e.g., `matplotlib` and others).

2. Use the MapReduce implementation from the lecture notes.

3. Create a `visualize_top_words` function for visualizing the results.

4. In the main code block:

- Fetch the text from the given URL.

- Apply MapReduce to analyze word frequency.

- Visualize the results.

**Acceptance Criteria**

- The code fetches text from a given URL.

- It analyzes word frequency using MapReduce.

- The visualization displays top words by frequency.

- The code employs multithreading.

- The code is readable and adheres to PEP 8 standards.

#### Result - Task 2

![Solution](./scr_task_02.png)

![Solution](./assets/report.png)